In [20]:
sns.catplot(data = interim_sqrt_df.loc[interim_sqrt_df['sess']==select_sess], x='interim_pass', kind='count')
plt.show()
select_sess = 2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.formula.api as smf
import statsmodels.api as sm
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from patsy import dmatrices
from numpy import arange
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from scipy.cluster import hierarchy
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
import time
CMPINF2120_EPM_FUNC_INCL_Over_Lisa.ipynb includes functions used in this notebook.
%run CMPINF2120_EPM_FUNC_INCL_Over_Lisa.ipynb
interim_sqrt_path = 'https://raw.githubusercontent.com/lisaover/CMPINF2120_project/main/tp_sqrt_inputs_interim_df.csv'
interim_sqrt_init = pd.read_csv(interim_sqrt_path)
interim_sqrt_init.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 3642 entries, 0 to 3641 Data columns (total 83 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 sess 3642 non-null int64 1 sid 3642 non-null int64 2 actv_grp 3642 non-null object 3 total_ms_tp000_sqrt 3642 non-null float64 4 mw_tp000_sqrt 3642 non-null float64 5 mwc_tp000_sqrt 3642 non-null float64 6 mcl_tp000_sqrt 3642 non-null float64 7 mcr_tp000_sqrt 3642 non-null float64 8 mm_tp000_sqrt 3642 non-null float64 9 ks_tp000_sqrt 3642 non-null float64 10 total_ms_tp010_sqrt 3642 non-null float64 11 mw_tp010_sqrt 3642 non-null float64 12 mwc_tp010_sqrt 3642 non-null float64 13 mcl_tp010_sqrt 3642 non-null float64 14 mcr_tp010_sqrt 3642 non-null float64 15 mm_tp010_sqrt 3642 non-null float64 16 ks_tp010_sqrt 3642 non-null float64 17 total_ms_tp020_sqrt 3642 non-null float64 18 mw_tp020_sqrt 3642 non-null float64 19 mwc_tp020_sqrt 3642 non-null float64 20 mcl_tp020_sqrt 3642 non-null float64 21 mcr_tp020_sqrt 3642 non-null float64 22 mm_tp020_sqrt 3642 non-null float64 23 ks_tp020_sqrt 3642 non-null float64 24 total_ms_tp030_sqrt 3642 non-null float64 25 mw_tp030_sqrt 3642 non-null float64 26 mwc_tp030_sqrt 3642 non-null float64 27 mcl_tp030_sqrt 3642 non-null float64 28 mcr_tp030_sqrt 3642 non-null float64 29 mm_tp030_sqrt 3642 non-null float64 30 ks_tp030_sqrt 3642 non-null float64 31 total_ms_tp040_sqrt 3642 non-null float64 32 mw_tp040_sqrt 3642 non-null float64 33 mwc_tp040_sqrt 3642 non-null float64 34 mcl_tp040_sqrt 3642 non-null float64 35 mcr_tp040_sqrt 3642 non-null float64 36 mm_tp040_sqrt 3642 non-null float64 37 ks_tp040_sqrt 3642 non-null float64 38 total_ms_tp050_sqrt 3642 non-null float64 39 mw_tp050_sqrt 3642 non-null float64 40 mwc_tp050_sqrt 3642 non-null float64 41 mcl_tp050_sqrt 3642 non-null float64 42 mcr_tp050_sqrt 3642 non-null float64 43 mm_tp050_sqrt 3642 non-null float64 44 ks_tp050_sqrt 3642 non-null float64 45 total_ms_tp060_sqrt 3642 non-null float64 46 mw_tp060_sqrt 3642 non-null float64 47 mwc_tp060_sqrt 3642 non-null float64 48 mcl_tp060_sqrt 3642 non-null float64 49 mcr_tp060_sqrt 3642 non-null float64 50 mm_tp060_sqrt 3642 non-null float64 51 ks_tp060_sqrt 3642 non-null float64 52 total_ms_tp070_sqrt 3642 non-null float64 53 mw_tp070_sqrt 3642 non-null float64 54 mwc_tp070_sqrt 3642 non-null float64 55 mcl_tp070_sqrt 3642 non-null float64 56 mcr_tp070_sqrt 3642 non-null float64 57 mm_tp070_sqrt 3642 non-null float64 58 ks_tp070_sqrt 3642 non-null float64 59 total_ms_tp080_sqrt 3642 non-null float64 60 mw_tp080_sqrt 3642 non-null float64 61 mwc_tp080_sqrt 3642 non-null float64 62 mcl_tp080_sqrt 3642 non-null float64 63 mcr_tp080_sqrt 3642 non-null float64 64 mm_tp080_sqrt 3642 non-null float64 65 ks_tp080_sqrt 3642 non-null float64 66 total_ms_tp090_sqrt 3642 non-null float64 67 mw_tp090_sqrt 3642 non-null float64 68 mwc_tp090_sqrt 3642 non-null float64 69 mcl_tp090_sqrt 3642 non-null float64 70 mcr_tp090_sqrt 3642 non-null float64 71 mm_tp090_sqrt 3642 non-null float64 72 ks_tp090_sqrt 3642 non-null float64 73 total_ms_tp100_sqrt 3642 non-null float64 74 mw_tp100_sqrt 3642 non-null float64 75 mwc_tp100_sqrt 3642 non-null float64 76 mcl_tp100_sqrt 3642 non-null float64 77 mcr_tp100_sqrt 3642 non-null float64 78 mm_tp100_sqrt 3642 non-null float64 79 ks_tp100_sqrt 3642 non-null float64 80 interim_scr 3642 non-null float64 81 max_interim_scr 3642 non-null float64 82 interim_pass 3642 non-null float64 dtypes: float64(80), int64(2), object(1) memory usage: 2.3+ MB
interim_sqrt_init.isna().sum()
sess 0
sid 0
actv_grp 0
total_ms_tp000_sqrt 0
mw_tp000_sqrt 0
..
mm_tp100_sqrt 0
ks_tp100_sqrt 0
interim_scr 0
max_interim_scr 0
interim_pass 0
Length: 83, dtype: int64
interim_sqrt_init['sid'] = interim_sqrt_init['sid'].astype('object') interim_sqrt_init['sess'] = interim_sqrt_init['sess'].astype('object')
interim_sqrt_df = interim_sqrt_init.copy()
sqrt_vars = get_var_list(interim_sqrt_df,['sqrt'])
totl_vars = get_var_list_b(interim_sqrt_df,['total'])
mw_vars = get_var_list_b(interim_sqrt_df,['mw_'])
mwc_vars = get_var_list_b(interim_sqrt_df,['mwc'])
mcl_vars = get_var_list_b(interim_sqrt_df,['mcl'])
mcr_vars = get_var_list_b(interim_sqrt_df,['mcr'])
mm_vars = get_var_list_b(interim_sqrt_df,['mm'])
ks_vars = get_var_list_b(interim_sqrt_df,['ks'])
features_df = interim_sqrt_df.loc[:, sqrt_vars].copy()
features_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 3642 entries, 0 to 3641 Data columns (total 77 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 total_ms_tp000_sqrt 3642 non-null float64 1 mw_tp000_sqrt 3642 non-null float64 2 mwc_tp000_sqrt 3642 non-null float64 3 mcl_tp000_sqrt 3642 non-null float64 4 mcr_tp000_sqrt 3642 non-null float64 5 mm_tp000_sqrt 3642 non-null float64 6 ks_tp000_sqrt 3642 non-null float64 7 total_ms_tp010_sqrt 3642 non-null float64 8 mw_tp010_sqrt 3642 non-null float64 9 mwc_tp010_sqrt 3642 non-null float64 10 mcl_tp010_sqrt 3642 non-null float64 11 mcr_tp010_sqrt 3642 non-null float64 12 mm_tp010_sqrt 3642 non-null float64 13 ks_tp010_sqrt 3642 non-null float64 14 total_ms_tp020_sqrt 3642 non-null float64 15 mw_tp020_sqrt 3642 non-null float64 16 mwc_tp020_sqrt 3642 non-null float64 17 mcl_tp020_sqrt 3642 non-null float64 18 mcr_tp020_sqrt 3642 non-null float64 19 mm_tp020_sqrt 3642 non-null float64 20 ks_tp020_sqrt 3642 non-null float64 21 total_ms_tp030_sqrt 3642 non-null float64 22 mw_tp030_sqrt 3642 non-null float64 23 mwc_tp030_sqrt 3642 non-null float64 24 mcl_tp030_sqrt 3642 non-null float64 25 mcr_tp030_sqrt 3642 non-null float64 26 mm_tp030_sqrt 3642 non-null float64 27 ks_tp030_sqrt 3642 non-null float64 28 total_ms_tp040_sqrt 3642 non-null float64 29 mw_tp040_sqrt 3642 non-null float64 30 mwc_tp040_sqrt 3642 non-null float64 31 mcl_tp040_sqrt 3642 non-null float64 32 mcr_tp040_sqrt 3642 non-null float64 33 mm_tp040_sqrt 3642 non-null float64 34 ks_tp040_sqrt 3642 non-null float64 35 total_ms_tp050_sqrt 3642 non-null float64 36 mw_tp050_sqrt 3642 non-null float64 37 mwc_tp050_sqrt 3642 non-null float64 38 mcl_tp050_sqrt 3642 non-null float64 39 mcr_tp050_sqrt 3642 non-null float64 40 mm_tp050_sqrt 3642 non-null float64 41 ks_tp050_sqrt 3642 non-null float64 42 total_ms_tp060_sqrt 3642 non-null float64 43 mw_tp060_sqrt 3642 non-null float64 44 mwc_tp060_sqrt 3642 non-null float64 45 mcl_tp060_sqrt 3642 non-null float64 46 mcr_tp060_sqrt 3642 non-null float64 47 mm_tp060_sqrt 3642 non-null float64 48 ks_tp060_sqrt 3642 non-null float64 49 total_ms_tp070_sqrt 3642 non-null float64 50 mw_tp070_sqrt 3642 non-null float64 51 mwc_tp070_sqrt 3642 non-null float64 52 mcl_tp070_sqrt 3642 non-null float64 53 mcr_tp070_sqrt 3642 non-null float64 54 mm_tp070_sqrt 3642 non-null float64 55 ks_tp070_sqrt 3642 non-null float64 56 total_ms_tp080_sqrt 3642 non-null float64 57 mw_tp080_sqrt 3642 non-null float64 58 mwc_tp080_sqrt 3642 non-null float64 59 mcl_tp080_sqrt 3642 non-null float64 60 mcr_tp080_sqrt 3642 non-null float64 61 mm_tp080_sqrt 3642 non-null float64 62 ks_tp080_sqrt 3642 non-null float64 63 total_ms_tp090_sqrt 3642 non-null float64 64 mw_tp090_sqrt 3642 non-null float64 65 mwc_tp090_sqrt 3642 non-null float64 66 mcl_tp090_sqrt 3642 non-null float64 67 mcr_tp090_sqrt 3642 non-null float64 68 mm_tp090_sqrt 3642 non-null float64 69 ks_tp090_sqrt 3642 non-null float64 70 total_ms_tp100_sqrt 3642 non-null float64 71 mw_tp100_sqrt 3642 non-null float64 72 mwc_tp100_sqrt 3642 non-null float64 73 mcl_tp100_sqrt 3642 non-null float64 74 mcr_tp100_sqrt 3642 non-null float64 75 mm_tp100_sqrt 3642 non-null float64 76 ks_tp100_sqrt 3642 non-null float64 dtypes: float64(77) memory usage: 2.1 MB
feature_names = features_df.columns
len(feature_names)
77
interim_sqrt_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 3642 entries, 0 to 3641 Data columns (total 83 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 sess 3642 non-null int64 1 sid 3642 non-null int64 2 actv_grp 3642 non-null object 3 total_ms_tp000_sqrt 3642 non-null float64 4 mw_tp000_sqrt 3642 non-null float64 5 mwc_tp000_sqrt 3642 non-null float64 6 mcl_tp000_sqrt 3642 non-null float64 7 mcr_tp000_sqrt 3642 non-null float64 8 mm_tp000_sqrt 3642 non-null float64 9 ks_tp000_sqrt 3642 non-null float64 10 total_ms_tp010_sqrt 3642 non-null float64 11 mw_tp010_sqrt 3642 non-null float64 12 mwc_tp010_sqrt 3642 non-null float64 13 mcl_tp010_sqrt 3642 non-null float64 14 mcr_tp010_sqrt 3642 non-null float64 15 mm_tp010_sqrt 3642 non-null float64 16 ks_tp010_sqrt 3642 non-null float64 17 total_ms_tp020_sqrt 3642 non-null float64 18 mw_tp020_sqrt 3642 non-null float64 19 mwc_tp020_sqrt 3642 non-null float64 20 mcl_tp020_sqrt 3642 non-null float64 21 mcr_tp020_sqrt 3642 non-null float64 22 mm_tp020_sqrt 3642 non-null float64 23 ks_tp020_sqrt 3642 non-null float64 24 total_ms_tp030_sqrt 3642 non-null float64 25 mw_tp030_sqrt 3642 non-null float64 26 mwc_tp030_sqrt 3642 non-null float64 27 mcl_tp030_sqrt 3642 non-null float64 28 mcr_tp030_sqrt 3642 non-null float64 29 mm_tp030_sqrt 3642 non-null float64 30 ks_tp030_sqrt 3642 non-null float64 31 total_ms_tp040_sqrt 3642 non-null float64 32 mw_tp040_sqrt 3642 non-null float64 33 mwc_tp040_sqrt 3642 non-null float64 34 mcl_tp040_sqrt 3642 non-null float64 35 mcr_tp040_sqrt 3642 non-null float64 36 mm_tp040_sqrt 3642 non-null float64 37 ks_tp040_sqrt 3642 non-null float64 38 total_ms_tp050_sqrt 3642 non-null float64 39 mw_tp050_sqrt 3642 non-null float64 40 mwc_tp050_sqrt 3642 non-null float64 41 mcl_tp050_sqrt 3642 non-null float64 42 mcr_tp050_sqrt 3642 non-null float64 43 mm_tp050_sqrt 3642 non-null float64 44 ks_tp050_sqrt 3642 non-null float64 45 total_ms_tp060_sqrt 3642 non-null float64 46 mw_tp060_sqrt 3642 non-null float64 47 mwc_tp060_sqrt 3642 non-null float64 48 mcl_tp060_sqrt 3642 non-null float64 49 mcr_tp060_sqrt 3642 non-null float64 50 mm_tp060_sqrt 3642 non-null float64 51 ks_tp060_sqrt 3642 non-null float64 52 total_ms_tp070_sqrt 3642 non-null float64 53 mw_tp070_sqrt 3642 non-null float64 54 mwc_tp070_sqrt 3642 non-null float64 55 mcl_tp070_sqrt 3642 non-null float64 56 mcr_tp070_sqrt 3642 non-null float64 57 mm_tp070_sqrt 3642 non-null float64 58 ks_tp070_sqrt 3642 non-null float64 59 total_ms_tp080_sqrt 3642 non-null float64 60 mw_tp080_sqrt 3642 non-null float64 61 mwc_tp080_sqrt 3642 non-null float64 62 mcl_tp080_sqrt 3642 non-null float64 63 mcr_tp080_sqrt 3642 non-null float64 64 mm_tp080_sqrt 3642 non-null float64 65 ks_tp080_sqrt 3642 non-null float64 66 total_ms_tp090_sqrt 3642 non-null float64 67 mw_tp090_sqrt 3642 non-null float64 68 mwc_tp090_sqrt 3642 non-null float64 69 mcl_tp090_sqrt 3642 non-null float64 70 mcr_tp090_sqrt 3642 non-null float64 71 mm_tp090_sqrt 3642 non-null float64 72 ks_tp090_sqrt 3642 non-null float64 73 total_ms_tp100_sqrt 3642 non-null float64 74 mw_tp100_sqrt 3642 non-null float64 75 mwc_tp100_sqrt 3642 non-null float64 76 mcl_tp100_sqrt 3642 non-null float64 77 mcr_tp100_sqrt 3642 non-null float64 78 mm_tp100_sqrt 3642 non-null float64 79 ks_tp100_sqrt 3642 non-null float64 80 interim_scr 3642 non-null float64 81 max_interim_scr 3642 non-null float64 82 interim_pass 3642 non-null float64 dtypes: float64(80), int64(2), object(1) memory usage: 2.3+ MB
sns.catplot(data = interim_sqrt_df.loc[interim_sqrt_df['sess']==select_sess], x='interim_pass', kind='count')
plt.show()
select_sess_df = interim_sqrt_df.loc[interim_sqrt_df['sess']==select_sess].drop(columns=['interim_scr','max_interim_scr']).copy()
select_sess_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 653 entries, 0 to 652 Data columns (total 81 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 sess 653 non-null int64 1 sid 653 non-null int64 2 actv_grp 653 non-null object 3 total_ms_tp000_sqrt 653 non-null float64 4 mw_tp000_sqrt 653 non-null float64 5 mwc_tp000_sqrt 653 non-null float64 6 mcl_tp000_sqrt 653 non-null float64 7 mcr_tp000_sqrt 653 non-null float64 8 mm_tp000_sqrt 653 non-null float64 9 ks_tp000_sqrt 653 non-null float64 10 total_ms_tp010_sqrt 653 non-null float64 11 mw_tp010_sqrt 653 non-null float64 12 mwc_tp010_sqrt 653 non-null float64 13 mcl_tp010_sqrt 653 non-null float64 14 mcr_tp010_sqrt 653 non-null float64 15 mm_tp010_sqrt 653 non-null float64 16 ks_tp010_sqrt 653 non-null float64 17 total_ms_tp020_sqrt 653 non-null float64 18 mw_tp020_sqrt 653 non-null float64 19 mwc_tp020_sqrt 653 non-null float64 20 mcl_tp020_sqrt 653 non-null float64 21 mcr_tp020_sqrt 653 non-null float64 22 mm_tp020_sqrt 653 non-null float64 23 ks_tp020_sqrt 653 non-null float64 24 total_ms_tp030_sqrt 653 non-null float64 25 mw_tp030_sqrt 653 non-null float64 26 mwc_tp030_sqrt 653 non-null float64 27 mcl_tp030_sqrt 653 non-null float64 28 mcr_tp030_sqrt 653 non-null float64 29 mm_tp030_sqrt 653 non-null float64 30 ks_tp030_sqrt 653 non-null float64 31 total_ms_tp040_sqrt 653 non-null float64 32 mw_tp040_sqrt 653 non-null float64 33 mwc_tp040_sqrt 653 non-null float64 34 mcl_tp040_sqrt 653 non-null float64 35 mcr_tp040_sqrt 653 non-null float64 36 mm_tp040_sqrt 653 non-null float64 37 ks_tp040_sqrt 653 non-null float64 38 total_ms_tp050_sqrt 653 non-null float64 39 mw_tp050_sqrt 653 non-null float64 40 mwc_tp050_sqrt 653 non-null float64 41 mcl_tp050_sqrt 653 non-null float64 42 mcr_tp050_sqrt 653 non-null float64 43 mm_tp050_sqrt 653 non-null float64 44 ks_tp050_sqrt 653 non-null float64 45 total_ms_tp060_sqrt 653 non-null float64 46 mw_tp060_sqrt 653 non-null float64 47 mwc_tp060_sqrt 653 non-null float64 48 mcl_tp060_sqrt 653 non-null float64 49 mcr_tp060_sqrt 653 non-null float64 50 mm_tp060_sqrt 653 non-null float64 51 ks_tp060_sqrt 653 non-null float64 52 total_ms_tp070_sqrt 653 non-null float64 53 mw_tp070_sqrt 653 non-null float64 54 mwc_tp070_sqrt 653 non-null float64 55 mcl_tp070_sqrt 653 non-null float64 56 mcr_tp070_sqrt 653 non-null float64 57 mm_tp070_sqrt 653 non-null float64 58 ks_tp070_sqrt 653 non-null float64 59 total_ms_tp080_sqrt 653 non-null float64 60 mw_tp080_sqrt 653 non-null float64 61 mwc_tp080_sqrt 653 non-null float64 62 mcl_tp080_sqrt 653 non-null float64 63 mcr_tp080_sqrt 653 non-null float64 64 mm_tp080_sqrt 653 non-null float64 65 ks_tp080_sqrt 653 non-null float64 66 total_ms_tp090_sqrt 653 non-null float64 67 mw_tp090_sqrt 653 non-null float64 68 mwc_tp090_sqrt 653 non-null float64 69 mcl_tp090_sqrt 653 non-null float64 70 mcr_tp090_sqrt 653 non-null float64 71 mm_tp090_sqrt 653 non-null float64 72 ks_tp090_sqrt 653 non-null float64 73 total_ms_tp100_sqrt 653 non-null float64 74 mw_tp100_sqrt 653 non-null float64 75 mwc_tp100_sqrt 653 non-null float64 76 mcl_tp100_sqrt 653 non-null float64 77 mcr_tp100_sqrt 653 non-null float64 78 mm_tp100_sqrt 653 non-null float64 79 ks_tp100_sqrt 653 non-null float64 80 interim_pass 653 non-null float64 dtypes: float64(78), int64(2), object(1) memory usage: 418.3+ KB
select_sess_df.isna().sum()
sess 0
sid 0
actv_grp 0
total_ms_tp000_sqrt 0
mw_tp000_sqrt 0
..
mcl_tp100_sqrt 0
mcr_tp100_sqrt 0
mm_tp100_sqrt 0
ks_tp100_sqrt 0
interim_pass 0
Length: 81, dtype: int64
features_df = select_sess_df.loc[:, sqrt_vars].copy()
features_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 653 entries, 0 to 652 Data columns (total 77 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 total_ms_tp000_sqrt 653 non-null float64 1 mw_tp000_sqrt 653 non-null float64 2 mwc_tp000_sqrt 653 non-null float64 3 mcl_tp000_sqrt 653 non-null float64 4 mcr_tp000_sqrt 653 non-null float64 5 mm_tp000_sqrt 653 non-null float64 6 ks_tp000_sqrt 653 non-null float64 7 total_ms_tp010_sqrt 653 non-null float64 8 mw_tp010_sqrt 653 non-null float64 9 mwc_tp010_sqrt 653 non-null float64 10 mcl_tp010_sqrt 653 non-null float64 11 mcr_tp010_sqrt 653 non-null float64 12 mm_tp010_sqrt 653 non-null float64 13 ks_tp010_sqrt 653 non-null float64 14 total_ms_tp020_sqrt 653 non-null float64 15 mw_tp020_sqrt 653 non-null float64 16 mwc_tp020_sqrt 653 non-null float64 17 mcl_tp020_sqrt 653 non-null float64 18 mcr_tp020_sqrt 653 non-null float64 19 mm_tp020_sqrt 653 non-null float64 20 ks_tp020_sqrt 653 non-null float64 21 total_ms_tp030_sqrt 653 non-null float64 22 mw_tp030_sqrt 653 non-null float64 23 mwc_tp030_sqrt 653 non-null float64 24 mcl_tp030_sqrt 653 non-null float64 25 mcr_tp030_sqrt 653 non-null float64 26 mm_tp030_sqrt 653 non-null float64 27 ks_tp030_sqrt 653 non-null float64 28 total_ms_tp040_sqrt 653 non-null float64 29 mw_tp040_sqrt 653 non-null float64 30 mwc_tp040_sqrt 653 non-null float64 31 mcl_tp040_sqrt 653 non-null float64 32 mcr_tp040_sqrt 653 non-null float64 33 mm_tp040_sqrt 653 non-null float64 34 ks_tp040_sqrt 653 non-null float64 35 total_ms_tp050_sqrt 653 non-null float64 36 mw_tp050_sqrt 653 non-null float64 37 mwc_tp050_sqrt 653 non-null float64 38 mcl_tp050_sqrt 653 non-null float64 39 mcr_tp050_sqrt 653 non-null float64 40 mm_tp050_sqrt 653 non-null float64 41 ks_tp050_sqrt 653 non-null float64 42 total_ms_tp060_sqrt 653 non-null float64 43 mw_tp060_sqrt 653 non-null float64 44 mwc_tp060_sqrt 653 non-null float64 45 mcl_tp060_sqrt 653 non-null float64 46 mcr_tp060_sqrt 653 non-null float64 47 mm_tp060_sqrt 653 non-null float64 48 ks_tp060_sqrt 653 non-null float64 49 total_ms_tp070_sqrt 653 non-null float64 50 mw_tp070_sqrt 653 non-null float64 51 mwc_tp070_sqrt 653 non-null float64 52 mcl_tp070_sqrt 653 non-null float64 53 mcr_tp070_sqrt 653 non-null float64 54 mm_tp070_sqrt 653 non-null float64 55 ks_tp070_sqrt 653 non-null float64 56 total_ms_tp080_sqrt 653 non-null float64 57 mw_tp080_sqrt 653 non-null float64 58 mwc_tp080_sqrt 653 non-null float64 59 mcl_tp080_sqrt 653 non-null float64 60 mcr_tp080_sqrt 653 non-null float64 61 mm_tp080_sqrt 653 non-null float64 62 ks_tp080_sqrt 653 non-null float64 63 total_ms_tp090_sqrt 653 non-null float64 64 mw_tp090_sqrt 653 non-null float64 65 mwc_tp090_sqrt 653 non-null float64 66 mcl_tp090_sqrt 653 non-null float64 67 mcr_tp090_sqrt 653 non-null float64 68 mm_tp090_sqrt 653 non-null float64 69 ks_tp090_sqrt 653 non-null float64 70 total_ms_tp100_sqrt 653 non-null float64 71 mw_tp100_sqrt 653 non-null float64 72 mwc_tp100_sqrt 653 non-null float64 73 mcl_tp100_sqrt 653 non-null float64 74 mcr_tp100_sqrt 653 non-null float64 75 mm_tp100_sqrt 653 non-null float64 76 ks_tp100_sqrt 653 non-null float64 dtypes: float64(77) memory usage: 397.9 KB
feature_names = features_df.columns
len(feature_names)
77
sns.catplot(data = select_sess_df, kind='box', aspect=3.5)
plt.show()
fig, ax = plt.subplots(figsize=(12, 8))
sns.heatmap(data = select_sess_df[feature_names].corr(),
vmin=-1, vmax=1, center = 0,
cmap='coolwarm',
ax=ax)
plt.show()
%run CMPINF2120_EPM_PCA_INCL_Over_Lisa.ipynb
<class 'pandas.core.frame.DataFrame'> RangeIndex: 653 entries, 0 to 652 Data columns (total 77 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PC01 653 non-null float64 1 PC02 653 non-null float64 2 PC03 653 non-null float64 3 PC04 653 non-null float64 4 PC05 653 non-null float64 5 PC06 653 non-null float64 6 PC07 653 non-null float64 7 PC08 653 non-null float64 8 PC09 653 non-null float64 9 PC10 653 non-null float64 10 PC11 653 non-null float64 11 PC12 653 non-null float64 12 PC13 653 non-null float64 13 PC14 653 non-null float64 14 PC15 653 non-null float64 15 PC16 653 non-null float64 16 PC17 653 non-null float64 17 PC18 653 non-null float64 18 PC19 653 non-null float64 19 PC20 653 non-null float64 20 PC21 653 non-null float64 21 PC22 653 non-null float64 22 PC23 653 non-null float64 23 PC24 653 non-null float64 24 PC25 653 non-null float64 25 PC26 653 non-null float64 26 PC27 653 non-null float64 27 PC28 653 non-null float64 28 PC29 653 non-null float64 29 PC30 653 non-null float64 30 PC31 653 non-null float64 31 PC32 653 non-null float64 32 PC33 653 non-null float64 33 PC34 653 non-null float64 34 PC35 653 non-null float64 35 PC36 653 non-null float64 36 PC37 653 non-null float64 37 PC38 653 non-null float64 38 PC39 653 non-null float64 39 PC40 653 non-null float64 40 PC41 653 non-null float64 41 PC42 653 non-null float64 42 PC43 653 non-null float64 43 PC44 653 non-null float64 44 PC45 653 non-null float64 45 PC46 653 non-null float64 46 PC47 653 non-null float64 47 PC48 653 non-null float64 48 PC49 653 non-null float64 49 PC50 653 non-null float64 50 PC51 653 non-null float64 51 PC52 653 non-null float64 52 PC53 653 non-null float64 53 PC54 653 non-null float64 54 PC55 653 non-null float64 55 PC56 653 non-null float64 56 PC57 653 non-null float64 57 PC58 653 non-null float64 58 PC59 653 non-null float64 59 PC60 653 non-null float64 60 PC61 653 non-null float64 61 PC62 653 non-null float64 62 PC63 653 non-null float64 63 PC64 653 non-null float64 64 PC65 653 non-null float64 65 PC66 653 non-null float64 66 PC67 653 non-null float64 67 PC68 653 non-null float64 68 PC69 653 non-null float64 69 PC70 653 non-null float64 70 PC71 653 non-null float64 71 PC72 653 non-null float64 72 PC73 653 non-null float64 73 PC74 653 non-null float64 74 PC75 653 non-null float64 75 PC76 653 non-null float64 76 PC77 653 non-null float64 dtypes: float64(77) memory usage: 392.9 KB
pc_scores_select_df = pc_scores_df.copy()
select_Xtimepoints = Xtimepoints.copy()
num_pcs = 12
pc_cluster_df = pd.concat([select_sess_df.loc[:,['sid','actv_grp','interim_pass']].copy(), pc_scores_select_df.copy()], axis=1)
pc_cluster_df.sid = pc_cluster_df.sid.astype('category')
sns.set_style('whitegrid')
sns.relplot(data = pc_cluster_df, x='PC01', y='PC02')
plt.show()
sns.set_style('whitegrid')
sns.relplot(data = pc_cluster_df, x='PC01', y='PC02', hue='actv_grp', legend=False)
plt.show()
sns.set_style('whitegrid')
sns.relplot(data = pc_cluster_df, x='PC01', y='PC02', hue='sid', legend=False)
plt.show()
sns.set_style('whitegrid')
sns.relplot(data = pc_cluster_df, x='PC01', y='PC02', hue='interim_pass', legend=False)
plt.show()
The optimal number of clusters is 4.
tots_within = []
K = range(1, 31)
for k in K:
km = KMeans(n_clusters=k, random_state=121, n_init=25, max_iter=500).fit( pc_scores_select_df )
tots_within.append( km.inertia_ )
pc_scores_12_df = pc_scores_select_df.iloc[:,:12].copy()
sns.catplot(data = pc_scores_12_df, kind='box')
<seaborn.axisgrid.FacetGrid at 0x7fa903144130>
The silhouette coefficient shows that 2 clusters are optimal.
sil_coef = []
K = range(2, 31)
for k in K:
k_label = KMeans(n_clusters=k, random_state=121, n_init=25, max_iter=500).fit_predict( pc_scores_12_df )
sil_coef.append( silhouette_score(pc_scores_12_df, k_label) )
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(K, sil_coef, 'o-')
ax.set_xlabel('number of clusters')
ax.set_ylabel('average silhouette coefficient')
plt.show()
The optimal number of clusters chosen from the dendrogram is 3 or 8.
hclust_ward = hierarchy.ward( pc_scores_12_df )
fig = plt.figure( figsize=(18,6) )
dn = hierarchy.dendrogram( hclust_ward, no_labels=True )
plt.show()
ward_cut_a = hierarchy.cut_tree( hclust_ward, n_clusters=12 )
ward_cut_a0 = hierarchy.cut_tree( hclust_ward, n_clusters=5 )
ward_cut_a.shape
(653, 1)
type( ward_cut_a )
numpy.ndarray
ward_cut_a.ravel()
array([ 0, 1, 1, 1, 1, 0, 1, 1, 2, 3, 3, 3, 3, 2, 2, 3, 2,
3, 3, 3, 3, 2, 2, 3, 4, 4, 4, 4, 5, 1, 4, 4, 4, 6,
6, 1, 6, 6, 0, 6, 6, 6, 6, 1, 0, 1, 0, 1, 1, 6, 1,
1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 5, 3, 3,
3, 5, 3, 5, 3, 1, 1, 1, 1, 1, 0, 1, 1, 2, 3, 3, 3,
3, 2, 3, 2, 3, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8,
8, 8, 8, 8, 8, 5, 3, 3, 3, 5, 3, 2, 3, 9, 9, 9, 9,
9, 9, 1, 1, 1, 1, 1, 0, 1, 5, 1, 7, 7, 7, 7, 7, 0,
7, 7, 1, 1, 1, 1, 1, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2,
0, 2, 7, 10, 10, 7, 10, 10, 10, 3, 3, 3, 3, 3, 2, 3, 3,
1, 6, 6, 1, 6, 1, 6, 2, 2, 3, 3, 3, 2, 3, 2, 2, 2,
2, 2, 2, 2, 0, 2, 1, 1, 1, 0, 1, 1, 1, 1, 4, 4, 4,
4, 4, 4, 4, 4, 1, 0, 1, 0, 1, 0, 1, 1, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 5, 0, 1, 1, 9, 9, 9, 9, 9, 9,
9, 9, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,
4, 4, 4, 5, 7, 7, 7, 5, 7, 7, 7, 6, 6, 6, 0, 6, 0,
6, 5, 6, 2, 3, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2,
2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 5, 6, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 3, 3, 3, 2, 2, 3, 0,
3, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 5, 5, 5, 0, 1,
1, 3, 3, 3, 3, 3, 2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6,
11, 11, 11, 11, 11, 11, 11, 11, 11, 8, 8, 8, 8, 8, 8, 8, 8,
1, 1, 1, 1, 1, 0, 0, 1, 6, 6, 6, 6, 1, 1, 6, 6, 10,
10, 10, 10, 10, 10, 6, 4, 4, 1, 4, 4, 6, 4, 2, 2, 2, 2,
2, 2, 2, 2, 7, 7, 7, 7, 7, 7, 7, 7, 0, 2, 1, 1, 1,
0, 2, 1, 5, 5, 5, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9, 9,
9, 9, 1, 1, 1, 0, 1, 0, 0, 5, 1, 1, 6, 6, 1, 0, 1,
1, 7, 7, 7, 7, 7, 7, 3, 3, 3, 3, 2, 3, 3, 3, 5, 2,
3, 2, 2, 2, 2, 2, 0, 1, 1, 1, 6, 0, 1, 5, 1, 1, 1,
1, 1, 1, 0, 1, 1, 7, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1,
1, 1, 1, 0, 0, 1, 5, 5, 3, 2, 5, 2, 3, 3, 7, 7, 7,
7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 0, 1, 1, 6, 6, 1, 6,
6, 6, 6, 6, 1, 1, 1, 1, 1, 0, 1, 1, 2, 2, 2, 2, 2,
0, 2, 2, 5, 5, 5, 5, 5, 5, 3, 3, 3, 2, 3, 3, 2, 3,
5, 1, 1, 0, 5, 0, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 8,
8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,
1, 1, 5, 1, 5, 1, 1])
pc_scores_12_df.index
RangeIndex(start=0, stop=653, step=1)
pc_scores_12_df['hclust_a'] = pd.Series( ward_cut_a.ravel(), index=pc_scores_12_df.index )
pc_scores_12_df['hclust_a'] = pc_scores_12_df.hclust_a.astype('category')
pc_scores_12_df['hclust_a0'] = pd.Series( ward_cut_a0.ravel(), index=pc_scores_12_df.index )
pc_scores_12_df['hclust_a0'] = pc_scores_12_df.hclust_a0.astype('category')
sns.catplot(data = pc_scores_12_df, x='hclust_a0', hue='hclust_a', kind='count')
<seaborn.axisgrid.FacetGrid at 0x7fa90063b910>
sns.relplot(data = pc_scores_12_df, x='PC01', y='PC02', hue='hclust_a')
plt.show()
sns.relplot(data = pc_scores_12_df, x='PC01', y='PC02', col='hclust_a0', hue='hclust_a')
plt.show()
select_sess_df to pc_cluster_df dataset (merge).¶pc_scores_outp_df = pd.concat([select_sess_df.loc[:,['sid','actv_grp','interim_pass']].copy(), pc_scores_12_df.copy()], axis=1)
pc_scores_outp_df.head()
| sid | actv_grp | interim_pass | PC01 | PC02 | PC03 | PC04 | PC05 | PC06 | PC07 | PC08 | PC09 | PC10 | PC11 | PC12 | hclust_a | hclust_a0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Aulaweb | 1.0 | 4.696728 | -0.217543 | 1.523679 | 3.816703 | 1.855665 | 1.192844 | 0.205416 | -0.605668 | 0.077515 | -1.237570 | -0.467555 | 0.102760 | 0 | 0 |
| 1 | 1 | Blank | 1.0 | 1.851483 | 0.108764 | -1.962132 | 0.971797 | 0.728020 | 1.277528 | 0.368094 | 0.547836 | 0.243289 | -1.992564 | 0.580493 | 0.122270 | 1 | 1 |
| 2 | 1 | Deeds | 1.0 | 1.752744 | 0.066318 | -2.772918 | 0.333047 | 0.325769 | 1.119129 | 0.497290 | 0.428038 | 0.289876 | -1.850254 | 0.133520 | 0.414914 | 1 | 1 |
| 3 | 1 | Diagram | 1.0 | 4.776729 | -0.529619 | -0.972399 | 1.725558 | 0.686277 | 0.577979 | 0.683349 | -0.344691 | 0.340548 | -1.346820 | 0.465911 | 0.798300 | 1 | 1 |
| 4 | 1 | Other | 1.0 | 3.481437 | -0.138485 | -1.318605 | 1.436803 | 0.790349 | 1.019270 | 0.502882 | -0.531409 | 0.217946 | -1.741124 | -0.777373 | -0.243610 | 1 | 1 |
pc_scores_outp_df.interim_pass.mean()
0.41960183767228176
pc_scores_outp_df.hclust_a.value_counts().reset_index()
| index | hclust_a | |
|---|---|---|
| 0 | 1 | 173 |
| 1 | 3 | 91 |
| 2 | 2 | 87 |
| 3 | 4 | 52 |
| 4 | 0 | 51 |
| 5 | 6 | 46 |
| 6 | 7 | 46 |
| 7 | 5 | 39 |
| 8 | 8 | 25 |
| 9 | 9 | 23 |
| 10 | 10 | 11 |
| 11 | 11 | 9 |
pc_scores_outp_df.hclust_a0.value_counts().reset_index()
| index | hclust_a0 | |
|---|---|---|
| 0 | 1 | 225 |
| 1 | 2 | 224 |
| 2 | 0 | 131 |
| 3 | 3 | 64 |
| 4 | 4 | 9 |
fig, ax = plt.subplots(figsize=(18,14))
sns.heatmap(pd.crosstab(pc_scores_outp_df.hclust_a, pc_scores_outp_df.interim_pass, margins=True),
annot=True, annot_kws={'size': 10}, fmt='g')
plt.show()
fig, ax = plt.subplots(figsize=(18,14))
sns.heatmap(pd.crosstab(pc_scores_outp_df.hclust_a0, pc_scores_outp_df.interim_pass, margins=True),
annot=True, annot_kws={'size': 10}, fmt='g')
plt.show()
pc_to_model_df = pd.concat([select_sess_df.loc[:,['sid','actv_grp','interim_pass']].copy(), pc_scores_select_df.copy()], axis=1)
pc_features = ['PC01','PC02','PC03','PC04','PC05','PC06','PC07','PC08','PC09','PC10','PC11','PC12']
pc_features_str = ''
for ix, x in enumerate(pc_features):
if ix == len(pc_features) - 1:
pc_features_str = pc_features_str + x
else:
pc_features_str = pc_features_str + x + ' + '
pc_features_str
'PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12'
pc_desc_formulas = ['interim_pass ~ sid'
,'interim_pass ~ sid + actv_grp'
,'interim_pass ~ sid + actv_grp + ' + pc_features_str
,'interim_pass ~ sid * (' + pc_features_str + ')'
,'interim_pass ~ sid * (actv_grp + ' + pc_features_str + ')'
]
pc_pred_formulas = ['interim_pass ~ ' + pc_features_str
,'interim_pass ~ (' + pc_features_str + ')**2'
,'interim_pass ~ actv_grp + ' + pc_features_str
,'interim_pass ~ actv_grp * (' + pc_features_str + ')'
,'interim_pass ~ actv_grp + (' + pc_features_str + ')**2'
,'interim_pass ~ actv_grp * (' + pc_features_str + ')**2'
]
formula_list = pc_desc_formulas + pc_pred_formulas
formula_list
['interim_pass ~ sid', 'interim_pass ~ sid + actv_grp', 'interim_pass ~ sid + actv_grp + PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12', 'interim_pass ~ sid * (PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12)', 'interim_pass ~ sid * (actv_grp + PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12)', 'interim_pass ~ PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12', 'interim_pass ~ (PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12)**2', 'interim_pass ~ actv_grp + PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12', 'interim_pass ~ actv_grp * (PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12)', 'interim_pass ~ actv_grp + (PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12)**2', 'interim_pass ~ actv_grp * (PC01 + PC02 + PC03 + PC04 + PC05 + PC06 + PC07 + PC08 + PC09 + PC10 + PC11 + PC12)**2']
sk_list = make_dmat(pc_to_model_df, formula_list)
model_dim = make_dim_df(pc_to_model_df, sk_list, formula_list)
model_dim
| model name | dimensions | number of obs | dim < obs | |
|---|---|---|---|---|
| 0 | 0 | 2 | 653 | Yes |
| 1 | 1 | 11 | 653 | Yes |
| 2 | 2 | 23 | 653 | Yes |
| 3 | 3 | 26 | 653 | Yes |
| 4 | 4 | 44 | 653 | Yes |
| 5 | 5 | 13 | 653 | Yes |
| 6 | 6 | 79 | 653 | Yes |
| 7 | 7 | 22 | 653 | Yes |
| 8 | 8 | 130 | 653 | Yes |
| 9 | 9 | 88 | 653 | Yes |
| 10 | 10 | 790 | 653 | No |
def fit_model(dm_y, dm_X):
return sm.GLM(dm_y, dm_X, family=sm.families.Binomial()).fit(bic_llf=True)
pc_dm00_y, pc_dm00_X = dmatrices(formula_list[0], data=pc_to_model_df, return_type='dataframe')
pc_dm01_y, pc_dm01_X = dmatrices(formula_list[1], data=pc_to_model_df, return_type='dataframe')
pc_dm02_y, pc_dm02_X = dmatrices(formula_list[2], data=pc_to_model_df, return_type='dataframe')
pc_dm03_y, pc_dm03_X = dmatrices(formula_list[3], data=pc_to_model_df, return_type='dataframe')
pc_dm04_y, pc_dm04_X = dmatrices(formula_list[4], data=pc_to_model_df, return_type='dataframe')
pc_dm05_y, pc_dm05_X = dmatrices(formula_list[5], data=pc_to_model_df, return_type='dataframe')
pc_dm06_y, pc_dm06_X = dmatrices(formula_list[6], data=pc_to_model_df, return_type='dataframe')
pc_dm07_y, pc_dm07_X = dmatrices(formula_list[7], data=pc_to_model_df, return_type='dataframe')
pc_dm08_y, pc_dm08_X = dmatrices(formula_list[8], data=pc_to_model_df, return_type='dataframe')
pc_dm09_y, pc_dm09_X = dmatrices(formula_list[9], data=pc_to_model_df, return_type='dataframe')
pc_dm10_y, pc_dm10_X = dmatrices(formula_list[10], data=pc_to_model_df, return_type='dataframe')
model_list = []
model_list.append( fit_model(pc_dm00_y, pc_dm00_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm01_y, pc_dm01_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm02_y, pc_dm02_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm03_y, pc_dm03_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm04_y, pc_dm04_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm05_y, pc_dm05_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm06_y, pc_dm06_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm07_y, pc_dm07_X) )
time.sleep(5)
model_list.append( fit_model(pc_dm08_y, pc_dm08_X) )
/Users/lisaover/opt/anaconda3/envs/cmpinf2120/lib/python3.8/site-packages/statsmodels/genmod/families/links.py:187: RuntimeWarning: overflow encountered in exp t = np.exp(-z)
time.sleep(5)
model_list.append( fit_model(pc_dm09_y, pc_dm09_X) )
Perfect separation detected, results not available
#time.sleep(5)
#model_list.append( fit_model(pc_dm10_y, pc_dm10_X) )
model_results = pd.DataFrame({'model_name': ['mod00','mod01','mod02','mod03','mod04','mod05','mod06','mod07','mod08','mod09'],
'AIC': [mod.aic for mod in model_list],
'BIC': [mod.bic for mod in model_list]})
/Users/lisaover/opt/anaconda3/envs/cmpinf2120/lib/python3.8/site-packages/statsmodels/genmod/families/family.py:1014: RuntimeWarning: divide by zero encountered in log special.gammaln(n - y + 1) + y * np.log(mu / (1 - mu + 1e-20)) + /Users/lisaover/opt/anaconda3/envs/cmpinf2120/lib/python3.8/site-packages/statsmodels/genmod/families/family.py:1014: RuntimeWarning: invalid value encountered in multiply special.gammaln(n - y + 1) + y * np.log(mu / (1 - mu + 1e-20)) + /Users/lisaover/opt/anaconda3/envs/cmpinf2120/lib/python3.8/site-packages/statsmodels/genmod/generalized_linear_model.py:1799: FutureWarning: The bic value is computed using the deviance formula. After 0.13 this will change to the log-likelihood based formula. This change has no impact on the relative rank of models compared using BIC. You can directly access the log-likelihood version using the `bic_llf` attribute. You can suppress this message by calling statsmodels.genmod.generalized_linear_model.SET_USE_BIC_LLF with True to get the LLF-based version now or False to retainthe deviance version. warnings.warn(
model_results
| model_name | AIC | BIC | |
|---|---|---|---|
| 0 | mod00 | 892.291197 | -3331.215514 |
| 1 | mod01 | 906.704377 | -3276.468140 |
| 2 | mod02 | 835.870558 | -3293.523034 |
| 3 | mod03 | 703.443078 | -3412.505782 |
| 4 | mod04 | 725.814320 | -3309.466151 |
| 5 | mod05 | 824.374147 | -3349.835215 |
| 6 | mod06 | 601.061981 | -3277.363291 |
| 7 | mod07 | 834.218919 | -3299.656250 |
| 8 | mod08 | NaN | 19196.275128 |
| 9 | mod09 | 579.486629 | -3258.604449 |
sns.relplot(data = model_results.melt(id_vars=['model_name']),
x='model_name',
y='value',
col='variable',
col_wrap=2,
facet_kws = {'sharey': False},
height=5, aspect=2)
plt.show()
select_sess_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 653 entries, 0 to 652 Data columns (total 81 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 sess 653 non-null int64 1 sid 653 non-null int64 2 actv_grp 653 non-null object 3 total_ms_tp000_sqrt 653 non-null float64 4 mw_tp000_sqrt 653 non-null float64 5 mwc_tp000_sqrt 653 non-null float64 6 mcl_tp000_sqrt 653 non-null float64 7 mcr_tp000_sqrt 653 non-null float64 8 mm_tp000_sqrt 653 non-null float64 9 ks_tp000_sqrt 653 non-null float64 10 total_ms_tp010_sqrt 653 non-null float64 11 mw_tp010_sqrt 653 non-null float64 12 mwc_tp010_sqrt 653 non-null float64 13 mcl_tp010_sqrt 653 non-null float64 14 mcr_tp010_sqrt 653 non-null float64 15 mm_tp010_sqrt 653 non-null float64 16 ks_tp010_sqrt 653 non-null float64 17 total_ms_tp020_sqrt 653 non-null float64 18 mw_tp020_sqrt 653 non-null float64 19 mwc_tp020_sqrt 653 non-null float64 20 mcl_tp020_sqrt 653 non-null float64 21 mcr_tp020_sqrt 653 non-null float64 22 mm_tp020_sqrt 653 non-null float64 23 ks_tp020_sqrt 653 non-null float64 24 total_ms_tp030_sqrt 653 non-null float64 25 mw_tp030_sqrt 653 non-null float64 26 mwc_tp030_sqrt 653 non-null float64 27 mcl_tp030_sqrt 653 non-null float64 28 mcr_tp030_sqrt 653 non-null float64 29 mm_tp030_sqrt 653 non-null float64 30 ks_tp030_sqrt 653 non-null float64 31 total_ms_tp040_sqrt 653 non-null float64 32 mw_tp040_sqrt 653 non-null float64 33 mwc_tp040_sqrt 653 non-null float64 34 mcl_tp040_sqrt 653 non-null float64 35 mcr_tp040_sqrt 653 non-null float64 36 mm_tp040_sqrt 653 non-null float64 37 ks_tp040_sqrt 653 non-null float64 38 total_ms_tp050_sqrt 653 non-null float64 39 mw_tp050_sqrt 653 non-null float64 40 mwc_tp050_sqrt 653 non-null float64 41 mcl_tp050_sqrt 653 non-null float64 42 mcr_tp050_sqrt 653 non-null float64 43 mm_tp050_sqrt 653 non-null float64 44 ks_tp050_sqrt 653 non-null float64 45 total_ms_tp060_sqrt 653 non-null float64 46 mw_tp060_sqrt 653 non-null float64 47 mwc_tp060_sqrt 653 non-null float64 48 mcl_tp060_sqrt 653 non-null float64 49 mcr_tp060_sqrt 653 non-null float64 50 mm_tp060_sqrt 653 non-null float64 51 ks_tp060_sqrt 653 non-null float64 52 total_ms_tp070_sqrt 653 non-null float64 53 mw_tp070_sqrt 653 non-null float64 54 mwc_tp070_sqrt 653 non-null float64 55 mcl_tp070_sqrt 653 non-null float64 56 mcr_tp070_sqrt 653 non-null float64 57 mm_tp070_sqrt 653 non-null float64 58 ks_tp070_sqrt 653 non-null float64 59 total_ms_tp080_sqrt 653 non-null float64 60 mw_tp080_sqrt 653 non-null float64 61 mwc_tp080_sqrt 653 non-null float64 62 mcl_tp080_sqrt 653 non-null float64 63 mcr_tp080_sqrt 653 non-null float64 64 mm_tp080_sqrt 653 non-null float64 65 ks_tp080_sqrt 653 non-null float64 66 total_ms_tp090_sqrt 653 non-null float64 67 mw_tp090_sqrt 653 non-null float64 68 mwc_tp090_sqrt 653 non-null float64 69 mcl_tp090_sqrt 653 non-null float64 70 mcr_tp090_sqrt 653 non-null float64 71 mm_tp090_sqrt 653 non-null float64 72 ks_tp090_sqrt 653 non-null float64 73 total_ms_tp100_sqrt 653 non-null float64 74 mw_tp100_sqrt 653 non-null float64 75 mwc_tp100_sqrt 653 non-null float64 76 mcl_tp100_sqrt 653 non-null float64 77 mcr_tp100_sqrt 653 non-null float64 78 mm_tp100_sqrt 653 non-null float64 79 ks_tp100_sqrt 653 non-null float64 80 interim_pass 653 non-null float64 dtypes: float64(78), int64(2), object(1) memory usage: 434.5+ KB
categorical_inputs = ['sid','actv_grp']
sqrt_vars
['total_ms_tp000_sqrt', 'mw_tp000_sqrt', 'mwc_tp000_sqrt', 'mcl_tp000_sqrt', 'mcr_tp000_sqrt', 'mm_tp000_sqrt', 'ks_tp000_sqrt', 'total_ms_tp010_sqrt', 'mw_tp010_sqrt', 'mwc_tp010_sqrt', 'mcl_tp010_sqrt', 'mcr_tp010_sqrt', 'mm_tp010_sqrt', 'ks_tp010_sqrt', 'total_ms_tp020_sqrt', 'mw_tp020_sqrt', 'mwc_tp020_sqrt', 'mcl_tp020_sqrt', 'mcr_tp020_sqrt', 'mm_tp020_sqrt', 'ks_tp020_sqrt', 'total_ms_tp030_sqrt', 'mw_tp030_sqrt', 'mwc_tp030_sqrt', 'mcl_tp030_sqrt', 'mcr_tp030_sqrt', 'mm_tp030_sqrt', 'ks_tp030_sqrt', 'total_ms_tp040_sqrt', 'mw_tp040_sqrt', 'mwc_tp040_sqrt', 'mcl_tp040_sqrt', 'mcr_tp040_sqrt', 'mm_tp040_sqrt', 'ks_tp040_sqrt', 'total_ms_tp050_sqrt', 'mw_tp050_sqrt', 'mwc_tp050_sqrt', 'mcl_tp050_sqrt', 'mcr_tp050_sqrt', 'mm_tp050_sqrt', 'ks_tp050_sqrt', 'total_ms_tp060_sqrt', 'mw_tp060_sqrt', 'mwc_tp060_sqrt', 'mcl_tp060_sqrt', 'mcr_tp060_sqrt', 'mm_tp060_sqrt', 'ks_tp060_sqrt', 'total_ms_tp070_sqrt', 'mw_tp070_sqrt', 'mwc_tp070_sqrt', 'mcl_tp070_sqrt', 'mcr_tp070_sqrt', 'mm_tp070_sqrt', 'ks_tp070_sqrt', 'total_ms_tp080_sqrt', 'mw_tp080_sqrt', 'mwc_tp080_sqrt', 'mcl_tp080_sqrt', 'mcr_tp080_sqrt', 'mm_tp080_sqrt', 'ks_tp080_sqrt', 'total_ms_tp090_sqrt', 'mw_tp090_sqrt', 'mwc_tp090_sqrt', 'mcl_tp090_sqrt', 'mcr_tp090_sqrt', 'mm_tp090_sqrt', 'ks_tp090_sqrt', 'total_ms_tp100_sqrt', 'mw_tp100_sqrt', 'mwc_tp100_sqrt', 'mcl_tp100_sqrt', 'mcr_tp100_sqrt', 'mm_tp100_sqrt', 'ks_tp100_sqrt']
categorical_transform = Pipeline( steps = [ ('dummy', OneHotEncoder(drop='first')) ] )
numeric_transform_pca = Pipeline( steps=[('std_input', StandardScaler()),
('pca', PCA(n_components=num_pcs)),
('std_features', StandardScaler())])
complex_transform = ColumnTransformer( transformers=[('num', numeric_transform_pca, sqrt_vars),
('cat', categorical_transform, categorical_inputs)])
complex_transform.transformers
[('num',
Pipeline(steps=[('std_input', StandardScaler()), ('pca', PCA(n_components=12)),
('std_features', StandardScaler())]),
['total_ms_tp000_sqrt',
'mw_tp000_sqrt',
'mwc_tp000_sqrt',
'mcl_tp000_sqrt',
'mcr_tp000_sqrt',
'mm_tp000_sqrt',
'ks_tp000_sqrt',
'total_ms_tp010_sqrt',
'mw_tp010_sqrt',
'mwc_tp010_sqrt',
'mcl_tp010_sqrt',
'mcr_tp010_sqrt',
'mm_tp010_sqrt',
'ks_tp010_sqrt',
'total_ms_tp020_sqrt',
'mw_tp020_sqrt',
'mwc_tp020_sqrt',
'mcl_tp020_sqrt',
'mcr_tp020_sqrt',
'mm_tp020_sqrt',
'ks_tp020_sqrt',
'total_ms_tp030_sqrt',
'mw_tp030_sqrt',
'mwc_tp030_sqrt',
'mcl_tp030_sqrt',
'mcr_tp030_sqrt',
'mm_tp030_sqrt',
'ks_tp030_sqrt',
'total_ms_tp040_sqrt',
'mw_tp040_sqrt',
'mwc_tp040_sqrt',
'mcl_tp040_sqrt',
'mcr_tp040_sqrt',
'mm_tp040_sqrt',
'ks_tp040_sqrt',
'total_ms_tp050_sqrt',
'mw_tp050_sqrt',
'mwc_tp050_sqrt',
'mcl_tp050_sqrt',
'mcr_tp050_sqrt',
'mm_tp050_sqrt',
'ks_tp050_sqrt',
'total_ms_tp060_sqrt',
'mw_tp060_sqrt',
'mwc_tp060_sqrt',
'mcl_tp060_sqrt',
'mcr_tp060_sqrt',
'mm_tp060_sqrt',
'ks_tp060_sqrt',
'total_ms_tp070_sqrt',
'mw_tp070_sqrt',
'mwc_tp070_sqrt',
'mcl_tp070_sqrt',
'mcr_tp070_sqrt',
'mm_tp070_sqrt',
'ks_tp070_sqrt',
'total_ms_tp080_sqrt',
'mw_tp080_sqrt',
'mwc_tp080_sqrt',
'mcl_tp080_sqrt',
'mcr_tp080_sqrt',
'mm_tp080_sqrt',
'ks_tp080_sqrt',
'total_ms_tp090_sqrt',
'mw_tp090_sqrt',
'mwc_tp090_sqrt',
'mcl_tp090_sqrt',
'mcr_tp090_sqrt',
'mm_tp090_sqrt',
'ks_tp090_sqrt',
'total_ms_tp100_sqrt',
'mw_tp100_sqrt',
'mwc_tp100_sqrt',
'mcl_tp100_sqrt',
'mcr_tp100_sqrt',
'mm_tp100_sqrt',
'ks_tp100_sqrt']),
('cat',
Pipeline(steps=[('dummy', OneHotEncoder(drop='first'))]),
['sid', 'actv_grp'])]
additive_transform = Pipeline( steps=[ ('prepro', complex_transform),
('make_poly', PolynomialFeatures(degree=1, interaction_only=True, include_bias=False))])
additive_transform.fit_transform(select_sess_df).shape
(653, 101)
len(sqrt_vars)
77
select_sess_df.shape
(653, 81)
select_sess_X = select_sess_df.drop(columns=['sess','interim_pass']).copy()
select_sess_df.interim_pass.value_counts()
0.0 379 1.0 274 Name: interim_pass, dtype: int64
select_sess_df.interim_pass.to_numpy().ravel()
array([1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,
1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0.,
0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,
0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1.,
1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,
1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,
1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,
1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.,
0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
1., 1., 1., 1., 1., 1., 1.])
select_sess_y = select_sess_df.interim_pass.to_numpy().ravel()
select_sess_X.shape
(653, 79)
select_sess_X.columns
Index(['sid', 'actv_grp', 'total_ms_tp000_sqrt', 'mw_tp000_sqrt',
'mwc_tp000_sqrt', 'mcl_tp000_sqrt', 'mcr_tp000_sqrt', 'mm_tp000_sqrt',
'ks_tp000_sqrt', 'total_ms_tp010_sqrt', 'mw_tp010_sqrt',
'mwc_tp010_sqrt', 'mcl_tp010_sqrt', 'mcr_tp010_sqrt', 'mm_tp010_sqrt',
'ks_tp010_sqrt', 'total_ms_tp020_sqrt', 'mw_tp020_sqrt',
'mwc_tp020_sqrt', 'mcl_tp020_sqrt', 'mcr_tp020_sqrt', 'mm_tp020_sqrt',
'ks_tp020_sqrt', 'total_ms_tp030_sqrt', 'mw_tp030_sqrt',
'mwc_tp030_sqrt', 'mcl_tp030_sqrt', 'mcr_tp030_sqrt', 'mm_tp030_sqrt',
'ks_tp030_sqrt', 'total_ms_tp040_sqrt', 'mw_tp040_sqrt',
'mwc_tp040_sqrt', 'mcl_tp040_sqrt', 'mcr_tp040_sqrt', 'mm_tp040_sqrt',
'ks_tp040_sqrt', 'total_ms_tp050_sqrt', 'mw_tp050_sqrt',
'mwc_tp050_sqrt', 'mcl_tp050_sqrt', 'mcr_tp050_sqrt', 'mm_tp050_sqrt',
'ks_tp050_sqrt', 'total_ms_tp060_sqrt', 'mw_tp060_sqrt',
'mwc_tp060_sqrt', 'mcl_tp060_sqrt', 'mcr_tp060_sqrt', 'mm_tp060_sqrt',
'ks_tp060_sqrt', 'total_ms_tp070_sqrt', 'mw_tp070_sqrt',
'mwc_tp070_sqrt', 'mcl_tp070_sqrt', 'mcr_tp070_sqrt', 'mm_tp070_sqrt',
'ks_tp070_sqrt', 'total_ms_tp080_sqrt', 'mw_tp080_sqrt',
'mwc_tp080_sqrt', 'mcl_tp080_sqrt', 'mcr_tp080_sqrt', 'mm_tp080_sqrt',
'ks_tp080_sqrt', 'total_ms_tp090_sqrt', 'mw_tp090_sqrt',
'mwc_tp090_sqrt', 'mcl_tp090_sqrt', 'mcr_tp090_sqrt', 'mm_tp090_sqrt',
'ks_tp090_sqrt', 'total_ms_tp100_sqrt', 'mw_tp100_sqrt',
'mwc_tp100_sqrt', 'mcl_tp100_sqrt', 'mcr_tp100_sqrt', 'mm_tp100_sqrt',
'ks_tp100_sqrt'],
dtype='object')
additive_transform.fit_transform( select_sess_X ).shape
(653, 101)
enet_to_fit = LogisticRegression(penalty='elasticnet', solver='saga', random_state=101, max_iter=25001, fit_intercept=True)
enet_grid = {'enet__C': np.exp(np.linspace(-6, 6, num=11)),
'enet__l1_ratio': np.linspace(0, 1, num=5)}
enet_additv_wflow = Pipeline( steps = [('prepro', additive_transform),
('enet', enet_to_fit)] )
my_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=101)
enet_additv_search = GridSearchCV( enet_additv_wflow, enet_grid, cv=my_cv)
enet_additv_search_results = enet_additv_search.fit(select_sess_X, select_sess_y)
enet_additv_search_results.best_params_
{'enet__C': 11.023176380641605, 'enet__l1_ratio': 0.25}
enet_additv_search_results.best_score_
1.0
enet_additv_search_results.best_estimator_.named_steps['enet'].coef_
array([[ 0.61089989, 0.27795982, -0.27477078, 0.14874456, -0.05045775,
0.3075289 , -0.97728891, -0.03718187, 0.46868514, -0.55774552,
-0.07856481, 0.022888 , -2.14181277, -3.07544824, 3.50124427,
3.35332434, 2.94305334, -3.71774753, -4.02770421, 2.6286713 ,
-3.82218762, -2.50552547, 4.56432194, -2.62347526, -1.7204614 ,
-2.66375703, 4.13638292, -5.50537852, -2.60009931, 5.12798023,
3.79322198, -3.15234746, -0.23141051, 3.97598264, -2.70267572,
-1.94269991, 2.91263303, 3.47503434, 3.82939812, -3.18796847,
-3.48101538, 2.03459272, -2.83272708, 4.15629066, 2.81900803,
-1.40444991, -3.01310385, -1.97693414, -4.32733173, -2.86949148,
-2.78469938, 3.72336412, -4.30561345, 3.34724042, -2.6039315 ,
-2.75383315, -3.46023178, -2.35976632, -3.86316577, 2.38468335,
5.20182576, 2.68177259, 3.05547232, 2.36962028, -3.93666854,
-2.83699619, -2.50102357, 3.89856457, -0.91994958, 0.94412968,
-4.02770396, -2.74022584, -0.97035484, 3.33827484, 3.05812732,
-4.72626129, 2.94405571, -3.4315306 , 3.37938586, -1.96356345,
-1.7413331 , 3.64643573, -3.7138137 , 3.05333701, -3.98600675,
-2.4826485 , -2.14546843, -4.15282255, 3.3391468 , 5.18163229,
-3.55670683, 3.43726797, -0.30048689, -0.33195458, -0.459645 ,
-0.1569932 , -0.09144763, -0.43592904, -0.12089857, -0.62655601,
-0.16607743]])
enet_additv_search_results.best_estimator_.named_steps['enet'].coef_.shape
(1, 101)
enet_additv_search_results.best_estimator_.named_steps['enet'].coef_.tolist()[0][85]
-2.4826485016623177
enet_additv_search_results.best_estimator_.named_steps['enet'].coef_[enet_additv_search_results.best_estimator_.named_steps['enet'].coef_ != 0].size
101
select_sess_df.shape
(653, 81)
select_sess_df.columns
Index(['sess', 'sid', 'actv_grp', 'total_ms_tp000_sqrt', 'mw_tp000_sqrt',
'mwc_tp000_sqrt', 'mcl_tp000_sqrt', 'mcr_tp000_sqrt', 'mm_tp000_sqrt',
'ks_tp000_sqrt', 'total_ms_tp010_sqrt', 'mw_tp010_sqrt',
'mwc_tp010_sqrt', 'mcl_tp010_sqrt', 'mcr_tp010_sqrt', 'mm_tp010_sqrt',
'ks_tp010_sqrt', 'total_ms_tp020_sqrt', 'mw_tp020_sqrt',
'mwc_tp020_sqrt', 'mcl_tp020_sqrt', 'mcr_tp020_sqrt', 'mm_tp020_sqrt',
'ks_tp020_sqrt', 'total_ms_tp030_sqrt', 'mw_tp030_sqrt',
'mwc_tp030_sqrt', 'mcl_tp030_sqrt', 'mcr_tp030_sqrt', 'mm_tp030_sqrt',
'ks_tp030_sqrt', 'total_ms_tp040_sqrt', 'mw_tp040_sqrt',
'mwc_tp040_sqrt', 'mcl_tp040_sqrt', 'mcr_tp040_sqrt', 'mm_tp040_sqrt',
'ks_tp040_sqrt', 'total_ms_tp050_sqrt', 'mw_tp050_sqrt',
'mwc_tp050_sqrt', 'mcl_tp050_sqrt', 'mcr_tp050_sqrt', 'mm_tp050_sqrt',
'ks_tp050_sqrt', 'total_ms_tp060_sqrt', 'mw_tp060_sqrt',
'mwc_tp060_sqrt', 'mcl_tp060_sqrt', 'mcr_tp060_sqrt', 'mm_tp060_sqrt',
'ks_tp060_sqrt', 'total_ms_tp070_sqrt', 'mw_tp070_sqrt',
'mwc_tp070_sqrt', 'mcl_tp070_sqrt', 'mcr_tp070_sqrt', 'mm_tp070_sqrt',
'ks_tp070_sqrt', 'total_ms_tp080_sqrt', 'mw_tp080_sqrt',
'mwc_tp080_sqrt', 'mcl_tp080_sqrt', 'mcr_tp080_sqrt', 'mm_tp080_sqrt',
'ks_tp080_sqrt', 'total_ms_tp090_sqrt', 'mw_tp090_sqrt',
'mwc_tp090_sqrt', 'mcl_tp090_sqrt', 'mcr_tp090_sqrt', 'mm_tp090_sqrt',
'ks_tp090_sqrt', 'total_ms_tp100_sqrt', 'mw_tp100_sqrt',
'mwc_tp100_sqrt', 'mcl_tp100_sqrt', 'mcr_tp100_sqrt', 'mm_tp100_sqrt',
'ks_tp100_sqrt', 'interim_pass'],
dtype='object')
fig, ax = plt.subplots(figsize=(24, 12))
sns.heatmap( data = np.transpose( calc_pca_contrib( pca_object.components_) ) > (1 / timepoint_pcs.shape[1]),
xticklabels=pc_scores_df.iloc[:, :len(feature_names)].columns.tolist(),
yticklabels=feature_names,
vmin=0, vmax=1)
plt.savefig('s2_pc_contrib.png')
plt.show()
select_sess_df.sid.unique()
array([ 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15,
16, 17, 18, 19, 20, 21, 22, 24, 27, 28, 29, 30, 32,
34, 36, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50,
51, 52, 53, 54, 56, 59, 61, 63, 66, 67, 70, 71, 72,
73, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
102, 103, 104])
select_sess_df.actv_grp.unique()
array(['Aulaweb', 'Blank', 'Deeds', 'Diagram', 'Other', 'Properties',
'Study', 'TextEditor', 'FSM_Related', 'Study_Materials'],
dtype=object)
input_grid = pd.DataFrame([ (xs,xag,xa0,xb0,xc0,xd0,xe0,xf0,xg0,xa1,xb1,xc1,xd1,xe1,xf1,xg1,xa2,xb2,xc2,xd2,xe2,xf2,xg2,xa3,xb3,xc3,xd3,xe3,xf3,xg3,xa4,xb4,xc4,xd4,xe4,xf4,xg4,xa5,xb5,xc5,xd5,xe5,xf5,xg5,xa6,xb6,xc6,xd6,xe6,xf6,xg6,xa7,xb7,xc7,xd7,xe7,xf7,xg7,xa8,xb8,xc8,xd8,xe8,xf8,xg8,xa9,xb9,xc9,xd9,xe9,xf9,xg9,xa10,xb10,xc10,xd10,xe10,xf10,xg10)
for xs in [5,20,41,56,79,93]
for xag in ['Aulaweb', 'Deeds', 'Diagram','TextEditor']
for xa0 in [select_sess_df.total_ms_tp000_sqrt.median()]
for xb0 in [select_sess_df.mw_tp000_sqrt.median()]
for xc0 in [select_sess_df.mwc_tp000_sqrt.median()]
for xd0 in [select_sess_df.mcl_tp000_sqrt.median()]
for xe0 in [select_sess_df.mcr_tp000_sqrt.median()]
for xf0 in [select_sess_df.mm_tp000_sqrt.median()]
for xg0 in [select_sess_df.ks_tp000_sqrt.median()]
for xa1 in [select_sess_df.total_ms_tp010_sqrt.median()]
for xb1 in [select_sess_df.mw_tp010_sqrt.median()]
for xc1 in [select_sess_df.mwc_tp010_sqrt.median()]
for xd1 in [select_sess_df.mcl_tp010_sqrt.median()]
for xe1 in [select_sess_df.mcr_tp010_sqrt.median()]
for xf1 in [select_sess_df.mm_tp010_sqrt.median()]
for xg1 in [select_sess_df.ks_tp010_sqrt.median()]
for xa2 in [select_sess_df.total_ms_tp020_sqrt.median()]
for xb2 in [select_sess_df.mw_tp020_sqrt.median()]
for xc2 in [select_sess_df.mwc_tp020_sqrt.median()]
for xd2 in [select_sess_df.mcl_tp020_sqrt.median()]
for xe2 in [select_sess_df.mcr_tp020_sqrt.median()]
for xf2 in [select_sess_df.mm_tp020_sqrt.median()]
for xg2 in [select_sess_df.ks_tp020_sqrt.median()]
for xa3 in [select_sess_df.total_ms_tp030_sqrt.median()]
for xb3 in [select_sess_df.mw_tp030_sqrt.median()]
for xc3 in [select_sess_df.mwc_tp030_sqrt.median()]
for xd3 in [select_sess_df.mcl_tp030_sqrt.median()]
for xe3 in [select_sess_df.mcr_tp030_sqrt.median()]
for xf3 in [select_sess_df.mm_tp030_sqrt.median()]
for xg3 in [select_sess_df.ks_tp030_sqrt.median()]
for xa4 in [select_sess_df.total_ms_tp040_sqrt.median()]
for xb4 in [select_sess_df.mw_tp040_sqrt.median()]
for xc4 in [select_sess_df.mwc_tp040_sqrt.median()]
for xd4 in [select_sess_df.mcl_tp040_sqrt.median()]
for xe4 in [select_sess_df.mcr_tp040_sqrt.median()]
for xf4 in [select_sess_df.mm_tp040_sqrt.median()]
for xg4 in [select_sess_df.ks_tp040_sqrt.median()]
for xa5 in [select_sess_df.total_ms_tp050_sqrt.median()]
for xb5 in [select_sess_df.mw_tp050_sqrt.median()]
for xc5 in [select_sess_df.mwc_tp050_sqrt.median()]
for xd5 in [select_sess_df.mcl_tp050_sqrt.median()]
for xe5 in [select_sess_df.mcr_tp050_sqrt.median()]
for xf5 in [select_sess_df.mm_tp050_sqrt.median()]
for xg5 in [select_sess_df.ks_tp050_sqrt.median()]
for xa6 in [select_sess_df.total_ms_tp060_sqrt.median()]
for xb6 in [select_sess_df.mw_tp060_sqrt.median()]
for xc6 in [select_sess_df.mwc_tp060_sqrt.median()]
for xd6 in [select_sess_df.mcl_tp060_sqrt.median()]
for xe6 in [select_sess_df.mcr_tp060_sqrt.median()]
for xf6 in [select_sess_df.mm_tp060_sqrt.median()]
for xg6 in [select_sess_df.ks_tp060_sqrt.median()]
for xa7 in [select_sess_df.total_ms_tp070_sqrt.median()]
for xb7 in [select_sess_df.mw_tp070_sqrt.median()]
for xc7 in [select_sess_df.mwc_tp070_sqrt.median()]
for xd7 in [select_sess_df.mcl_tp070_sqrt.median()]
for xe7 in [select_sess_df.mcr_tp070_sqrt.median()]
for xf7 in [select_sess_df.mm_tp070_sqrt.median()]
for xg7 in [select_sess_df.ks_tp070_sqrt.median()]
for xa8 in [select_sess_df.total_ms_tp080_sqrt.median()]
for xb8 in [select_sess_df.mw_tp080_sqrt.median()]
for xc8 in [select_sess_df.mwc_tp080_sqrt.median()]
for xd8 in [select_sess_df.mcl_tp080_sqrt.median()]
for xe8 in [select_sess_df.mcr_tp080_sqrt.median()]
for xf8 in [select_sess_df.mm_tp080_sqrt.median()]
for xg8 in [select_sess_df.ks_tp080_sqrt.median()]
for xa9 in [select_sess_df.total_ms_tp090_sqrt.median()]
for xb9 in [select_sess_df.mw_tp090_sqrt.median()]
for xc9 in [select_sess_df.mwc_tp090_sqrt.median()]
for xd9 in [select_sess_df.mcl_tp090_sqrt.median()]
for xe9 in [select_sess_df.mcr_tp090_sqrt.median()]
for xf9 in [select_sess_df.mm_tp090_sqrt.median()]
for xg9 in [select_sess_df.ks_tp090_sqrt.median()]
for xa10 in [select_sess_df.total_ms_tp100_sqrt.median()]
for xb10 in [select_sess_df.mw_tp100_sqrt.median()]
for xc10 in [select_sess_df.mwc_tp100_sqrt.median()]
for xd10 in [select_sess_df.mcl_tp100_sqrt.median()]
for xe10 in [select_sess_df.mcr_tp100_sqrt.median()]
for xf10 in np.linspace(select_sess_df.mm_tp100_sqrt.min() - 0.02, select_sess_df.mm_tp100_sqrt.max() + 0.02, num=101)
for xg10 in [select_sess_df.ks_tp100_sqrt.median()]],
columns=['sid', 'actv_grp', 'total_ms_tp000_sqrt', 'mw_tp000_sqrt',
'mwc_tp000_sqrt', 'mcl_tp000_sqrt', 'mcr_tp000_sqrt', 'mm_tp000_sqrt',
'ks_tp000_sqrt', 'total_ms_tp010_sqrt', 'mw_tp010_sqrt',
'mwc_tp010_sqrt', 'mcl_tp010_sqrt', 'mcr_tp010_sqrt', 'mm_tp010_sqrt',
'ks_tp010_sqrt', 'total_ms_tp020_sqrt', 'mw_tp020_sqrt',
'mwc_tp020_sqrt', 'mcl_tp020_sqrt', 'mcr_tp020_sqrt', 'mm_tp020_sqrt',
'ks_tp020_sqrt', 'total_ms_tp030_sqrt', 'mw_tp030_sqrt',
'mwc_tp030_sqrt', 'mcl_tp030_sqrt', 'mcr_tp030_sqrt', 'mm_tp030_sqrt',
'ks_tp030_sqrt', 'total_ms_tp040_sqrt', 'mw_tp040_sqrt',
'mwc_tp040_sqrt', 'mcl_tp040_sqrt', 'mcr_tp040_sqrt', 'mm_tp040_sqrt',
'ks_tp040_sqrt', 'total_ms_tp050_sqrt', 'mw_tp050_sqrt',
'mwc_tp050_sqrt', 'mcl_tp050_sqrt', 'mcr_tp050_sqrt', 'mm_tp050_sqrt',
'ks_tp050_sqrt', 'total_ms_tp060_sqrt', 'mw_tp060_sqrt',
'mwc_tp060_sqrt', 'mcl_tp060_sqrt', 'mcr_tp060_sqrt', 'mm_tp060_sqrt',
'ks_tp060_sqrt', 'total_ms_tp070_sqrt', 'mw_tp070_sqrt',
'mwc_tp070_sqrt', 'mcl_tp070_sqrt', 'mcr_tp070_sqrt', 'mm_tp070_sqrt',
'ks_tp070_sqrt', 'total_ms_tp080_sqrt', 'mw_tp080_sqrt',
'mwc_tp080_sqrt', 'mcl_tp080_sqrt', 'mcr_tp080_sqrt', 'mm_tp080_sqrt',
'ks_tp080_sqrt', 'total_ms_tp090_sqrt', 'mw_tp090_sqrt',
'mwc_tp090_sqrt', 'mcl_tp090_sqrt', 'mcr_tp090_sqrt', 'mm_tp090_sqrt',
'ks_tp090_sqrt', 'total_ms_tp100_sqrt', 'mw_tp100_sqrt',
'mwc_tp100_sqrt', 'mcl_tp100_sqrt', 'mcr_tp100_sqrt', 'mm_tp100_sqrt',
'ks_tp100_sqrt'])
input_grid.shape
(2424, 79)
input_grid.head()
| sid | actv_grp | total_ms_tp000_sqrt | mw_tp000_sqrt | mwc_tp000_sqrt | mcl_tp000_sqrt | mcr_tp000_sqrt | mm_tp000_sqrt | ks_tp000_sqrt | total_ms_tp010_sqrt | ... | mcr_tp090_sqrt | mm_tp090_sqrt | ks_tp090_sqrt | total_ms_tp100_sqrt | mw_tp100_sqrt | mwc_tp100_sqrt | mcl_tp100_sqrt | mcr_tp100_sqrt | mm_tp100_sqrt | ks_tp100_sqrt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5 | Aulaweb | 252.982213 | 0.0 | 0.0 | 5.09902 | 0.0 | 43.081318 | 0.0 | 850.646813 | ... | 13.928388 | 368.785032 | 58.379791 | 2866.007676 | 30.88689 | 0.0 | 55.497748 | 15.297059 | 7.121428 | 61.237244 |
| 1 | 5 | Aulaweb | 252.982213 | 0.0 | 0.0 | 5.09902 | 0.0 | 43.081318 | 0.0 | 850.646813 | ... | 13.928388 | 368.785032 | 58.379791 | 2866.007676 | 30.88689 | 0.0 | 55.497748 | 15.297059 | 16.823551 | 61.237244 |
| 2 | 5 | Aulaweb | 252.982213 | 0.0 | 0.0 | 5.09902 | 0.0 | 43.081318 | 0.0 | 850.646813 | ... | 13.928388 | 368.785032 | 58.379791 | 2866.007676 | 30.88689 | 0.0 | 55.497748 | 15.297059 | 26.525673 | 61.237244 |
| 3 | 5 | Aulaweb | 252.982213 | 0.0 | 0.0 | 5.09902 | 0.0 | 43.081318 | 0.0 | 850.646813 | ... | 13.928388 | 368.785032 | 58.379791 | 2866.007676 | 30.88689 | 0.0 | 55.497748 | 15.297059 | 36.227796 | 61.237244 |
| 4 | 5 | Aulaweb | 252.982213 | 0.0 | 0.0 | 5.09902 | 0.0 | 43.081318 | 0.0 | 850.646813 | ... | 13.928388 | 368.785032 | 58.379791 | 2866.007676 | 30.88689 | 0.0 | 55.497748 | 15.297059 | 45.929918 | 61.237244 |
5 rows × 79 columns
select_sess_X.head()
| sid | actv_grp | total_ms_tp000_sqrt | mw_tp000_sqrt | mwc_tp000_sqrt | mcl_tp000_sqrt | mcr_tp000_sqrt | mm_tp000_sqrt | ks_tp000_sqrt | total_ms_tp010_sqrt | ... | mcr_tp090_sqrt | mm_tp090_sqrt | ks_tp090_sqrt | total_ms_tp100_sqrt | mw_tp100_sqrt | mwc_tp100_sqrt | mcl_tp100_sqrt | mcr_tp100_sqrt | mm_tp100_sqrt | ks_tp100_sqrt | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Aulaweb | 1236.122971 | 16.643317 | 0.0 | 25.278449 | 1.414214 | 224.784341 | 37.496667 | 1552.288633 | ... | 15.349267 | 486.501593 | 51.314715 | 2760.796986 | 37.775654 | 0.0 | 61.212744 | 16.309506 | 520.037499 | 54.387499 |
| 1 | 1 | Blank | 216.794834 | 2.645751 | 0.0 | 4.582576 | 0.000000 | 46.808119 | 2.828427 | 1177.794549 | ... | 15.192103 | 494.317509 | 52.801515 | 2930.699575 | 38.483763 | 0.0 | 65.069194 | 17.720045 | 550.932845 | 57.113921 |
| 2 | 1 | Deeds | 70.710678 | 0.000000 | 0.0 | 2.449490 | 0.000000 | 21.260292 | 0.000000 | 1052.710786 | ... | 15.297059 | 501.163047 | 54.166410 | 2928.310093 | 38.470768 | 0.0 | 64.992307 | 17.720045 | 550.207234 | 56.920998 |
| 3 | 1 | Diagram | 604.979338 | 4.472136 | 0.0 | 14.832397 | 0.000000 | 117.085439 | 14.352700 | 1408.474352 | ... | 16.601205 | 525.389950 | 54.896266 | 2927.968579 | 38.470768 | 0.0 | 64.946132 | 17.720045 | 549.960908 | 56.920998 |
| 4 | 1 | Other | 771.362431 | 4.472136 | 0.0 | 16.852300 | 0.000000 | 151.650915 | 14.352700 | 1208.759695 | ... | 16.309506 | 521.050669 | 54.721111 | 2933.087111 | 38.483763 | 0.0 | 65.306967 | 17.720045 | 552.080610 | 57.410800 |
5 rows × 79 columns
input_grid_copy = input_grid.copy()
input_grid_copy['pred_enet_additv_class'] = enet_additv_search_results.predict( input_grid )
sns.relplot(data = input_grid_copy, x='mm_tp100_sqrt', y='pred_enet_additv_class', hue='actv_grp',
col='sid', col_wrap=2, kind='line')
plt.show()
categorical_inputs_b = ['actv_grp']
categorical_transform_b = Pipeline( steps = [ ('dummy', OneHotEncoder(drop='first')) ] )
Use numeric_transform_pca from above
complex_transform_b = ColumnTransformer( transformers=[('num', numeric_transform_pca, sqrt_vars),
('cat', categorical_transform_b, categorical_inputs_b)])
complex_transform_b.transformers
[('num',
Pipeline(steps=[('std_input', StandardScaler()), ('pca', PCA(n_components=12)),
('std_features', StandardScaler())]),
['total_ms_tp000_sqrt',
'mw_tp000_sqrt',
'mwc_tp000_sqrt',
'mcl_tp000_sqrt',
'mcr_tp000_sqrt',
'mm_tp000_sqrt',
'ks_tp000_sqrt',
'total_ms_tp010_sqrt',
'mw_tp010_sqrt',
'mwc_tp010_sqrt',
'mcl_tp010_sqrt',
'mcr_tp010_sqrt',
'mm_tp010_sqrt',
'ks_tp010_sqrt',
'total_ms_tp020_sqrt',
'mw_tp020_sqrt',
'mwc_tp020_sqrt',
'mcl_tp020_sqrt',
'mcr_tp020_sqrt',
'mm_tp020_sqrt',
'ks_tp020_sqrt',
'total_ms_tp030_sqrt',
'mw_tp030_sqrt',
'mwc_tp030_sqrt',
'mcl_tp030_sqrt',
'mcr_tp030_sqrt',
'mm_tp030_sqrt',
'ks_tp030_sqrt',
'total_ms_tp040_sqrt',
'mw_tp040_sqrt',
'mwc_tp040_sqrt',
'mcl_tp040_sqrt',
'mcr_tp040_sqrt',
'mm_tp040_sqrt',
'ks_tp040_sqrt',
'total_ms_tp050_sqrt',
'mw_tp050_sqrt',
'mwc_tp050_sqrt',
'mcl_tp050_sqrt',
'mcr_tp050_sqrt',
'mm_tp050_sqrt',
'ks_tp050_sqrt',
'total_ms_tp060_sqrt',
'mw_tp060_sqrt',
'mwc_tp060_sqrt',
'mcl_tp060_sqrt',
'mcr_tp060_sqrt',
'mm_tp060_sqrt',
'ks_tp060_sqrt',
'total_ms_tp070_sqrt',
'mw_tp070_sqrt',
'mwc_tp070_sqrt',
'mcl_tp070_sqrt',
'mcr_tp070_sqrt',
'mm_tp070_sqrt',
'ks_tp070_sqrt',
'total_ms_tp080_sqrt',
'mw_tp080_sqrt',
'mwc_tp080_sqrt',
'mcl_tp080_sqrt',
'mcr_tp080_sqrt',
'mm_tp080_sqrt',
'ks_tp080_sqrt',
'total_ms_tp090_sqrt',
'mw_tp090_sqrt',
'mwc_tp090_sqrt',
'mcl_tp090_sqrt',
'mcr_tp090_sqrt',
'mm_tp090_sqrt',
'ks_tp090_sqrt',
'total_ms_tp100_sqrt',
'mw_tp100_sqrt',
'mwc_tp100_sqrt',
'mcl_tp100_sqrt',
'mcr_tp100_sqrt',
'mm_tp100_sqrt',
'ks_tp100_sqrt']),
('cat',
Pipeline(steps=[('dummy', OneHotEncoder(drop='first'))]),
['actv_grp'])]
interact_transform = Pipeline( steps=[ ('prepro', complex_transform_b),
('make_poly', PolynomialFeatures(degree=2, interaction_only=True, include_bias=False))])
interact_transform.fit_transform(select_sess_df).shape
(653, 231)
select_sess_X_b = select_sess_df.drop(columns=['sess','sid','interim_pass']).copy()
select_sess_X.shape
(653, 79)
select_sess_X_b.shape
(653, 78)
select_sess_X_b.actv_grp.unique()
array(['Aulaweb', 'Blank', 'Deeds', 'Diagram', 'Other', 'Properties',
'Study', 'TextEditor', 'FSM_Related', 'Study_Materials'],
dtype=object)
len(select_sess_X_b.actv_grp.unique())
10
select_sess_df.interim_pass.value_counts()
0.0 379 1.0 274 Name: interim_pass, dtype: int64
Use same select_sess_y as above
interact_transform.fit_transform( select_sess_X_b ).shape
(653, 231)
Use enet_to_fit, enet_grid, and my_cv from above
enet_interact_wflow = Pipeline( steps = [('prepro', interact_transform),
('enet', enet_to_fit)] )
enet_interact_search = GridSearchCV( enet_interact_wflow, enet_grid, cv=my_cv)
enet_interact_search_results = enet_interact_search.fit(select_sess_X_b, select_sess_y)
enet_interact_search_results.best_params_
{'enet__C': 0.301194211912202, 'enet__l1_ratio': 1.0}
enet_interact_search_results.best_score_
0.7794128009395185
enet_interact_search_results.best_estimator_.named_steps['enet'].coef_
array([[ 0. , 0. , -0.69361576, 0.5683175 , 0. ,
0. , -1.16178095, 0. , 0.2011758 , -0.35614139,
0. , 0. , 0. , -0.14966677, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , -0.0442238 , 0. , -0.19493677, 0.13637333,
0.80075277, 0. , 0.13073142, -0.72424148, 0.00156989,
-0.22468978, -0.26951958, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.4038471 , -0.2057219 , -0.02016262, 0.0629105 ,
0.02587327, -0.16104088, 0. , -0.17740373, 0. ,
-0.05988025, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.23858038, -0.13483961, -0.41894843, 0.22166462, -0.42305627,
0.29877283, 0.13265667, 0.07353326, -0.00184862, 0. ,
0. , 0. , 0. , 0. , -0.11327241,
0. , 0. , 0. , 0.03838572, -0.15986539,
-0.23790839, 0.18298684, 0.73156134, -0.05383329, -0.10337803,
-0.05587731, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , -0.01706607, 0. ,
0.38381184, 0.20290021, 0. , -0.68993496, 0.078688 ,
0.24821585, 0.07462701, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.10660551, 0.07683004, -0.12362148, 0.08006732,
-0.24900545, 0.12356782, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.60727156, 0.02798063, -0.20689595,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01078861, -0.12065511, 0. , 0.2278609 , 0. ,
0. , 0. , 0.54246327, 0. , -0.33890179,
0.05301158, 0. , 0. , -0.19634929, -0.13195145,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.31474838, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.06952229, 0.13943218, 0. , 0. ,
0. , -0.1529835 , 0. , 0. , 0. ,
0. , 0. , 0. , 0.11088384, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ]])
enet_interact_search_results.best_estimator_.named_steps['enet'].coef_.shape
(1, 231)
enet_interact_search_results.best_estimator_.named_steps['enet'].coef_[enet_interact_search_results.best_estimator_.named_steps['enet'].coef_ != 0].size
70
input_grid_b = pd.DataFrame([ (xag,xa0,xb0,xc0,xd0,xe0,xf0,xg0,xa1,xb1,xc1,xd1,xe1,xf1,xg1,xa2,xb2,xc2,xd2,xe2,xf2,xg2,xa3,xb3,xc3,xd3,xe3,xf3,xg3,xa4,xb4,xc4,xd4,xe4,xf4,xg4,xa5,xb5,xc5,xd5,xe5,xf5,xg5,xa6,xb6,xc6,xd6,xe6,xf6,xg6,xa7,xb7,xc7,xd7,xe7,xf7,xg7,xa8,xb8,xc8,xd8,xe8,xf8,xg8,xa9,xb9,xc9,xd9,xe9,xf9,xg9,xa10,xb10,xc10,xd10,xe10,xf10,xg10)
for xag in select_sess_df.actv_grp.unique()
for xa0 in [select_sess_df.total_ms_tp000_sqrt.median()]
for xb0 in [select_sess_df.mw_tp000_sqrt.median()]
for xc0 in [select_sess_df.mwc_tp000_sqrt.median()]
for xd0 in [select_sess_df.mcl_tp000_sqrt.median()]
for xe0 in [select_sess_df.mcr_tp000_sqrt.median()]
for xf0 in [select_sess_df.mm_tp000_sqrt.median()]
for xg0 in [select_sess_df.ks_tp000_sqrt.median()]
for xa1 in [select_sess_df.total_ms_tp010_sqrt.median()]
for xb1 in [select_sess_df.mw_tp010_sqrt.median()]
for xc1 in [select_sess_df.mwc_tp010_sqrt.median()]
for xd1 in [select_sess_df.mcl_tp010_sqrt.median()]
for xe1 in [select_sess_df.mcr_tp010_sqrt.median()]
for xf1 in [select_sess_df.mm_tp010_sqrt.median()]
for xg1 in [select_sess_df.ks_tp010_sqrt.median()]
for xa2 in [select_sess_df.total_ms_tp020_sqrt.median()]
for xb2 in [select_sess_df.mw_tp020_sqrt.median()]
for xc2 in [select_sess_df.mwc_tp020_sqrt.median()]
for xd2 in [select_sess_df.mcl_tp020_sqrt.median()]
for xe2 in [select_sess_df.mcr_tp020_sqrt.median()]
for xf2 in [select_sess_df.mm_tp020_sqrt.median()]
for xg2 in [select_sess_df.ks_tp020_sqrt.median()]
for xa3 in [select_sess_df.total_ms_tp030_sqrt.median()]
for xb3 in [select_sess_df.mw_tp030_sqrt.median()]
for xc3 in [select_sess_df.mwc_tp030_sqrt.median()]
for xd3 in [select_sess_df.mcl_tp030_sqrt.median()]
for xe3 in [select_sess_df.mcr_tp030_sqrt.median()]
for xf3 in [select_sess_df.mm_tp030_sqrt.median()]
for xg3 in [select_sess_df.ks_tp030_sqrt.median()]
for xa4 in [select_sess_df.total_ms_tp040_sqrt.median()]
for xb4 in [select_sess_df.mw_tp040_sqrt.median()]
for xc4 in [select_sess_df.mwc_tp040_sqrt.median()]
for xd4 in [select_sess_df.mcl_tp040_sqrt.median()]
for xe4 in [select_sess_df.mcr_tp040_sqrt.median()]
for xf4 in [select_sess_df.mm_tp040_sqrt.median()]
for xg4 in [select_sess_df.ks_tp040_sqrt.median()]
for xa5 in [select_sess_df.total_ms_tp050_sqrt.median()]
for xb5 in [select_sess_df.mw_tp050_sqrt.median()]
for xc5 in [select_sess_df.mwc_tp050_sqrt.median()]
for xd5 in [select_sess_df.mcl_tp050_sqrt.median()]
for xe5 in [select_sess_df.mcr_tp050_sqrt.median()]
for xf5 in [select_sess_df.mm_tp050_sqrt.median()]
for xg5 in [select_sess_df.ks_tp050_sqrt.median()]
for xa6 in [select_sess_df.total_ms_tp060_sqrt.median()]
for xb6 in [select_sess_df.mw_tp060_sqrt.median()]
for xc6 in [select_sess_df.mwc_tp060_sqrt.median()]
for xd6 in [select_sess_df.mcl_tp060_sqrt.median()]
for xe6 in [select_sess_df.mcr_tp060_sqrt.median()]
for xf6 in [select_sess_df.mm_tp060_sqrt.median()]
for xg6 in [select_sess_df.ks_tp060_sqrt.median()]
for xa7 in [select_sess_df.total_ms_tp070_sqrt.median()]
for xb7 in [select_sess_df.mw_tp070_sqrt.median()]
for xc7 in [select_sess_df.mwc_tp070_sqrt.median()]
for xd7 in [select_sess_df.mcl_tp070_sqrt.median()]
for xe7 in [select_sess_df.mcr_tp070_sqrt.median()]
for xf7 in [select_sess_df.mm_tp070_sqrt.median()]
for xg7 in [select_sess_df.ks_tp070_sqrt.median()]
for xa8 in [select_sess_df.total_ms_tp080_sqrt.median()]
for xb8 in [select_sess_df.mw_tp080_sqrt.median()]
for xc8 in [select_sess_df.mwc_tp080_sqrt.median()]
for xd8 in [select_sess_df.mcl_tp080_sqrt.median()]
for xe8 in [select_sess_df.mcr_tp080_sqrt.median()]
for xf8 in [select_sess_df.mm_tp080_sqrt.median()]
for xg8 in [select_sess_df.ks_tp080_sqrt.median()]
for xa9 in [select_sess_df.total_ms_tp090_sqrt.median()]
for xb9 in [select_sess_df.mw_tp090_sqrt.median()]
for xc9 in [select_sess_df.mwc_tp090_sqrt.median()]
for xd9 in [select_sess_df.mcl_tp090_sqrt.median()]
for xe9 in [select_sess_df.mcr_tp090_sqrt.median()]
for xf9 in [select_sess_df.mm_tp090_sqrt.median()]
for xg9 in [select_sess_df.ks_tp090_sqrt.median()]
for xa10 in [select_sess_df.total_ms_tp100_sqrt.median()]
for xb10 in [select_sess_df.mw_tp100_sqrt.median()]
for xc10 in [select_sess_df.mwc_tp100_sqrt.median()]
for xd10 in [select_sess_df.mcl_tp100_sqrt.median()]
for xe10 in [select_sess_df.mcr_tp100_sqrt.median()]
for xf10 in np.linspace(select_sess_df.mm_tp100_sqrt.min() - 0.02, select_sess_df.mm_tp100_sqrt.max() + 0.02, num=101)
for xg10 in [select_sess_df.ks_tp100_sqrt.median()]],
columns=['actv_grp', 'total_ms_tp000_sqrt', 'mw_tp000_sqrt',
'mwc_tp000_sqrt', 'mcl_tp000_sqrt', 'mcr_tp000_sqrt', 'mm_tp000_sqrt',
'ks_tp000_sqrt', 'total_ms_tp010_sqrt', 'mw_tp010_sqrt',
'mwc_tp010_sqrt', 'mcl_tp010_sqrt', 'mcr_tp010_sqrt', 'mm_tp010_sqrt',
'ks_tp010_sqrt', 'total_ms_tp020_sqrt', 'mw_tp020_sqrt',
'mwc_tp020_sqrt', 'mcl_tp020_sqrt', 'mcr_tp020_sqrt', 'mm_tp020_sqrt',
'ks_tp020_sqrt', 'total_ms_tp030_sqrt', 'mw_tp030_sqrt',
'mwc_tp030_sqrt', 'mcl_tp030_sqrt', 'mcr_tp030_sqrt', 'mm_tp030_sqrt',
'ks_tp030_sqrt', 'total_ms_tp040_sqrt', 'mw_tp040_sqrt',
'mwc_tp040_sqrt', 'mcl_tp040_sqrt', 'mcr_tp040_sqrt', 'mm_tp040_sqrt',
'ks_tp040_sqrt', 'total_ms_tp050_sqrt', 'mw_tp050_sqrt',
'mwc_tp050_sqrt', 'mcl_tp050_sqrt', 'mcr_tp050_sqrt', 'mm_tp050_sqrt',
'ks_tp050_sqrt', 'total_ms_tp060_sqrt', 'mw_tp060_sqrt',
'mwc_tp060_sqrt', 'mcl_tp060_sqrt', 'mcr_tp060_sqrt', 'mm_tp060_sqrt',
'ks_tp060_sqrt', 'total_ms_tp070_sqrt', 'mw_tp070_sqrt',
'mwc_tp070_sqrt', 'mcl_tp070_sqrt', 'mcr_tp070_sqrt', 'mm_tp070_sqrt',
'ks_tp070_sqrt', 'total_ms_tp080_sqrt', 'mw_tp080_sqrt',
'mwc_tp080_sqrt', 'mcl_tp080_sqrt', 'mcr_tp080_sqrt', 'mm_tp080_sqrt',
'ks_tp080_sqrt', 'total_ms_tp090_sqrt', 'mw_tp090_sqrt',
'mwc_tp090_sqrt', 'mcl_tp090_sqrt', 'mcr_tp090_sqrt', 'mm_tp090_sqrt',
'ks_tp090_sqrt', 'total_ms_tp100_sqrt', 'mw_tp100_sqrt',
'mwc_tp100_sqrt', 'mcl_tp100_sqrt', 'mcr_tp100_sqrt', 'mm_tp100_sqrt',
'ks_tp100_sqrt'])
input_grid_b_copy = input_grid_b.copy()
input_grid_b_copy['pred_enet_interact_class'] = enet_interact_search_results.predict( input_grid_b )
sns.relplot(data = input_grid_b_copy, x='mm_tp100_sqrt', y='pred_enet_interact_class',
col='actv_grp', col_wrap=2, hue='actv_grp', kind='line')
plt.show()
s2_pc_scores_12_df = pc_scores_12_df.copy()
%store s2_pc_scores_12_df
Stored 's2_pc_scores_12_df' (DataFrame)
s2_pc_scores_outp_df = pc_scores_outp_df.copy()
%store s2_pc_scores_outp_df
Stored 's2_pc_scores_outp_df' (DataFrame)
s2_additv_sid_model_coef = enet_additv_search_results.best_estimator_.named_steps['enet'].coef_
%store s2_additv_sid_model_coef
Stored 's2_additv_sid_model_coef' (ndarray)
s2_interact_nosid_model_coef = enet_interact_search_results.best_estimator_.named_steps['enet'].coef_
%store s2_interact_nosid_model_coef
Stored 's2_interact_nosid_model_coef' (ndarray)
s2_additv_sid_model_params = enet_additv_search_results.best_params_
%store s2_additv_sid_model_params
Stored 's2_additv_sid_model_params' (dict)
s2_interact_nosid_model_params = enet_interact_search_results.best_params_
%store s2_interact_nosid_model_params
Stored 's2_interact_nosid_model_params' (dict)
s2_additv_sid_model_score = enet_additv_search_results.best_score_
%store s2_additv_sid_model_score
Stored 's2_additv_sid_model_score' (float64)
s2_interact_nosid_model_score = enet_interact_search_results.best_score_
%store s2_interact_nosid_model_score
Stored 's2_interact_nosid_model_score' (float64)
s2_input_grid_copy = input_grid_copy.copy()
%store s2_input_grid_copy
Stored 's2_input_grid_copy' (DataFrame)
s2_input_grid_b_copy = input_grid_b_copy.copy()
%store s2_input_grid_b_copy
Stored 's2_input_grid_b_copy' (DataFrame)